code: def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(data.x, data.edge_index, data.batch)
          loss = criterion(out.view(-1), data.y.to(torch.float))
          loss.backward()
          optimizer.step()
          total_loss += float(loss) * data.num_graphs
  
      return total_loss / len(train_dataset)
  
imports: import math
  import os.path
  import time
  from itertools import chain
  import numpy
  import torch
  import torch.nn.functional
  from scipy.sparse.csgraph import shortest_path
  from sklearn.metrics import roc_auc_score
  from torch.nn import BCEWithLogitsLoss, Conv1d, MaxPool1d, ModuleList
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.datasets import Planetoid
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GCNConv, SortAggregation
  from torch_geometric.transforms import RandomLinkSplit
  from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown